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Brand Name : Shi Zun
Model Number : JP-1126
Place of Origin : China
MOQ : Negotiable
Price : Negotiable
Supply Ability : 100+pcs+day
Delivery Time : 10 working days
Packaging Details : 84.0mm × 22.45mm × 19.35mm
Payment Terms : T/T
Processor : Quad-core ARM Cortex-A7 32-bit, 1.5GHz, with integrated NEON and FPU Each core has a 32KB I-cache and 32KB D-cache, plus 512KB shared L2 cache Based on RISC-V MCU
NPU : Up to 2.0 Tops performance, supports INT8/INT16, strong network model compatibility, RKNN model conversion tool available for converting common AI framework models (e.g., Caffe, Darknet, MXNet, ONNX, PyTorch, TensorFlow, TFLite) and algorithm support
Memory : 1GB/2GBDDR4
Storage : 8GB/16GB eMMC
Video Encoding : 4KH.264/H.26530fps 3840 x 2160@30fps + 720p@30fps encoding
Video Decoding : 4KH.264/H.26530fps 3840 x 2160@30encoding + 3840 x 2160@30fps decoding
System Support : Linux
Power : 5V/1A
Operating Temperature : -10℃~60℃
Operating Humidity : 10%~90%
Binocular Camera : Camera(IR)/ Camera(RGB)
Image Sensors : GC2053 / GC2093
Sensor Size : 1 / 2.9
Resolution : Center 800 Edge 600
Pixel Size : 2.8 μm
Output Format : RAW
Interface : MIPI
Focusing Distance : 80 cm
Lens : 4P
Optical Filter : 850 nm
Field of View : D70°H62°V38°
Optical Distortion : ≤0.5%
Focal Length : F2.0/4.3mm
Maximum Database : 100,000
Recommended Database : 10,000
Face Recognition Accuracy : Standard Testing Environment, 10,000-person Database: Without Mask: False Acceptance Rate: 0.01%; Recognition Accuracy: 99% With Mask: False Acceptance Rate: 0.01%; Recognition Accuracy: 95%
Face Detection : Face Detection Time: ~23 ms / Face Tracking Time: ~7 ms
Liveness Detection : Monocular Liveness Detection Time: ~45 ms / Binocular Liveness Detection Time: ~15 ms
Face Comparison : Feature Extraction Time: ~25 ms / Single Comparison Time: ~0.0115 ms
Recommended Image : 720P
Minimum Face Size for Recognition : Without Liveness Detection: 50 x 50 pixels / With Liveness Detection: 90 x 90 pixels)
Recommended Face Recognition Angles : Yaw: ≤ ±30° Pitch: ≤ ±30° Roll: ≤ ±30°
Module Size : 84.0mm × 22.45mm × 19.35mm
Enclosure Design : Aluminum alloy material with serrated heat sink back cover for efficient cooling
Power Consumption : Typical Power Consumption: 2.8W (5V, 560mA) / Maximum Power Consumption: 4.3W (5V, 860mA) / Minimum Power Consumption: 0.71W (5V, 142mA) / Power Supply Recommendation: 5V/1.2A or higher
JP1126 Intelligent Dual-Lens Camera Module Up to 2.0 Tops performance, supports INT8/INT16 5V/1A
JP1126 Intelligent Dual-Lens Camera Module Features:
JP1126 Intelligent Dual-Lens Camera Module Parameter:
Processor: | Quad-core ARM Cortex-A7 32-bit, 1.5GHz, with integrated NEON and FPU Each core has a 32KB I-cache and 32KB D-cache, plus 512KB shared L2 cache Based on RISC-V MCU |
NPU: | Up to 2.0 Tops performance, supports INT8/INT16, strong network model compatibility, RKNN model conversion tool available for converting common AI framework models (e.g., Caffe, Darknet, MXNet, ONNX, PyTorch, TensorFlow, TFLite) and algorithm support |
Memory: | 1GB/2GBDDR4 |
Storage: | 8GB/16GB eMMC |
Video encoding: | 4KH.264/H.26530fps 3840x2160@30fps+720p@30fpsencoding |
Video Decoding: | 4KH.264/H.26530fps 3840x2160@30encoding+3840x2160@30fpsdecoding |
System support: | Linux |
Power: | 5V/1A |
Image Sensors: | GC2053 GC2093 |
Module Board Dimensions: | 80* 16* 17.6mm (L* W* H) |
Resolution: | 1920*1080 |
Pixel Size: | 2.8 μm |
Interface: | MIPI |
Focal Length: | F2.0/4.3mm |
Maximum Database: | 100,000 |
Face Recognition Accuracy: | Standard Testing Environment, 10,000-person Database: Without Mask: False Acceptance Rate: 0.01%; Recognition Accuracy: 99% With Mask: False Acceptance Rate: 0.01%; Recognition Accuracy: 95% |
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JP1126 Intelligent Dual-Lens Camera Module Up to 2.0 Tops performance, supports INT8/INT16 5V/1A Images |